SAA-C03 Design High-Performing Architectures Practice Question
This SAA-C03 practice question tests your understanding of design high-performing architectures. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. A key principle to apply: target tracking scaling maintains a specified metric value.. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company runs a stateless web API on Amazon EC2 behind an Application Load Balancer. The team notices that during business hours, the ALB starts queueing requests and the average request latency rises. They want to scale out quickly and reliably based on demand, not CPU alone. Which Auto Scaling approach best matches this requirement?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use target tracking scaling based on ALB request count per target.
Option B is correct because target tracking scaling based on ALB request count per target directly measures the load on each instance, allowing the Auto Scaling group to add or remove instances to maintain a target value. This approach scales out quickly and reliably based on actual demand (request queuing and latency), not just CPU, which aligns with the requirement to respond to rising latency and queueing during business hours.
Key principle: Target tracking scaling maintains a specified metric value.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Use a fixed-size Auto Scaling group and increase capacity manually once per hour.
Why it's wrong here
This is manual and slow, which can’t react to sudden traffic spikes effectively.
✓
Use target tracking scaling based on ALB request count per target.
Why this is correct
Target tracking can automatically adjust capacity using ALB load metrics and respond faster.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Target tracking scaling maintains a specified metric value.
✗
Scale based only on EC2 instance memory utilization, regardless of load.
Why it's wrong here
Memory utilization may not correlate with queueing or request latency, causing mismatched scaling.
✗
Use step scaling with a single threshold on average network-in bytes.
Why it's wrong here
Step scaling can work, but networking thresholds often lag behind request queuing and latency issues.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often default to CPU-based scaling (a common but incomplete metric) or memory-based scaling, overlooking that for a stateless web API behind an ALB, request count per target is the most direct indicator of demand and latency issues.
Detailed technical explanation
How to think about this question
Target tracking scaling uses a predefined or custom metric (like ALBRequestCountPerTarget) and automatically adjusts the desired capacity to keep the metric near the target value, using a proportional-integral-derivative (PID) control algorithm to avoid oscillations. Under the hood, the ALB emits the RequestCountPerTarget metric to CloudWatch every minute, and the Auto Scaling group evaluates this metric to trigger scale-out or scale-in activities, typically within 2-3 minutes. In a real-world scenario, if the target is set to 1000 requests per target, the group will add instances when the average exceeds that threshold, preventing queue buildup and latency spikes.
KKey Concepts to Remember
Target tracking scaling maintains a specified metric value.
It uses a scaling policy to adjust capacity automatically.
ALB request count per target is a direct measure of application load.
Target tracking is proactive and responsive to demand changes.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Target tracking scaling maintains a specified metric value.
Real-world example
How this comes up in practice
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Got this wrong? Here's your next step.
Review target tracking scaling maintains a specified metric value., then practise related SAA-C03 questions on the same topic to reinforce the concept.
Design High-Performing Architectures — This question tests Design High-Performing Architectures — Target tracking scaling maintains a specified metric value..
What is the correct answer to this question?
The correct answer is: Use target tracking scaling based on ALB request count per target. — Option B is correct because target tracking scaling based on ALB request count per target directly measures the load on each instance, allowing the Auto Scaling group to add or remove instances to maintain a target value. This approach scales out quickly and reliably based on actual demand (request queuing and latency), not just CPU, which aligns with the requirement to respond to rising latency and queueing during business hours.
What should I do if I get this SAA-C03 question wrong?
Review target tracking scaling maintains a specified metric value., then practise related SAA-C03 questions on the same topic to reinforce the concept.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Target tracking scaling maintains a specified metric value.
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